{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "vscode": {
     "languageId": "plaintext"
    }
   },
   "source": [
    "# Try out the NVIDIA Multimodal PDF Data Extraction Blueprint!\n",
    "\n",
    "Welcome!\n",
    "\n",
    "In this notebook, we will run inference on the new NVIDIA Multimodal PDF Data Extraction Blueprint. This blueprint uses a state-of-the-art multimodal model to extract data from PDFs, PowerPoints, and images. \n",
    "\n",
    "\n",
    "**Important Notes**: \n",
    "1. In order to run this notebook, you need to visit NGC and get a key for the Multimodal PDF Data Extraction Blueprint."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction\n",
    "\n",
    "NVIDIA-Ingest is a scalable, performance-oriented document content and metadata extraction microservice. For more information, see [What is NVIDIA Ingest?](https://docs.nvidia.com/nemo/retriever/extraction/overview/)."
   ]
  },
  {
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    }
   },
   "cell_type": "markdown",
   "metadata": {
    "vscode": {
     "languageId": "plaintext"
    }
   },
   "source": [
    "# Architecture\n",
    "\n",
    "This blueprint is comprised of multiple pieces that come together to form a powerful multi-modal RAG solution\n",
    "\n",
    "![arch](attachment:b947a5fe-b01b-4f18-82e7-fda0c3a19c1a.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "vscode": {
     "languageId": "plaintext"
    }
   },
   "source": [
    "# Getting started"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Clone the repository and log into Docker\n",
    "\n",
    "In order to spin up this blueprint, you will need an NGC api key. Talk to your NVIDIA rep or apply for access at [https://developer.nvidia.com/nemo-microservices](https://developer.nvidia.com/nemo-microservices). After you get your API key, paste it below where we run `export NGC_API_KEY=`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Already on '25.6.2'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Your branch is up to date with 'origin/25.6.2'.\n",
      "Login Succeeded\n",
      ".env file has been created successfully.\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "\n",
    "[ -d \"nv-ingest\" ] || git clone https://github.com/nvidia/nv-ingest\n",
    "\n",
    "cd nv-ingest\n",
    "git checkout 25.6.2\n",
    "\n",
    "export NGC_API_KEY=<enter-key-here>\n",
    "\n",
    "echo \"${NGC_API_KEY}\" | docker login nvcr.io -u '$oauthtoken' --password-stdin\n",
    "\n",
    "cat << EOF > .env\n",
    "NGC_API_KEY=$NGC_API_KEY\n",
    "DATASET_ROOT=/home/ubuntu/verb-workspace/data\n",
    "NV_INGEST_ROOT=/home/ubuntu/verb-workspace/nv-ingest\n",
    "EOF\n",
    "\n",
    "echo \".env file has been created successfully.\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Spin up the blueprint\n",
    "\n",
    "NOTE: This step can take about 10 minutes. The following steps are how we suggest running and monitoring your the progress. If you do not want to monitor - you can simply run `docker compose --profile retrieval --profile table-structure up -d` in a new terminal window\n",
    "\n",
    "1. Open up a new terminal window and run the following\n",
    "\n",
    "```bash\n",
    "cd nv-ingest\n",
    "docker compose --profile retrieval --profile table-structure up\n",
    "```\n",
    "\n",
    "This will run each service and output persistant logs.\n",
    "\n",
    "2. In a second terminal window, run\n",
    "\n",
    "```bash\n",
    "cd nv-ingest\n",
    "docker compose logs -f nv-ingest-ms-runtime\n",
    "```\n",
    "\n",
    "This will show you persistant logs for the main `nv-ingest` service"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Checking for completion\n",
    "Things should be spun up properly if the first part of your `nv-ingest-ms-runtime` logs show something similar to \n",
    "\n",
    "```bash\n",
    "nv-ingest-ms-runtime-1  | INFO:     Uvicorn running on http://0.0.0.0:7670 (Press CTRL+C to quit)\n",
    "nv-ingest-ms-runtime-1  | INFO:     Started parent process [20]\n",
    "nv-ingest-ms-runtime-1  | INFO:     Started server process [40]\n",
    "```\n",
    "\n",
    "and the final lines look similar to \n",
    "\n",
    "```bash\n",
    "nv-ingest-ms-runtime-1  | 2024-10-16 02:36:11,162 - DEBUG - parent_receive started child_thread\n",
    "nv-ingest-ms-runtime-1  | 2024-10-16 02:36:11,162 - DEBUG - parent_receive started child_thread\n",
    "nv-ingest-ms-runtime-1  | 2024-10-16 02:36:11,163 - DEBUG - parent_receive started child_thread\n",
    "```\n",
    "\n",
    "After everything is up and running, we can run `docker ps` and `nvidia-smi` "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "!docker ps"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After you run `docker ps` you should see output similar to the following that lists your container images and the status of each. If any status includes `starting`, wait for the container to start before you proceed.\n",
    "\n",
    "```text\n",
    "CONTAINER ID   IMAGE                                        COMMAND                 CREATED            STATUS                      PORTS          \n",
    "9869e432cc04   zilliz/attu:v2.3.5                           \"docker-entrypoint.s…\"  About an hour ago  Up About an hour            0.0.0.0:3001...\n",
    "e02baf85ccc5   otel/opentelemetry-collector-contrib:0.91.0  \"/otelcol-contrib --…\"  About an hour ago  Up About an hour            0.0.0.0:4317...\n",
    "4c3be36de11b   milvusdb/milvus:v2.5.3-gpu                   \"/tini -- milvus run…\"  About an hour ago  Up About an hour (healthy)  0.0.0.0:9091...\n",
    "...\n",
    "```\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Tue Jun 17 18:11:58 2025       \n",
      "+-----------------------------------------------------------------------------------------+\n",
      "| NVIDIA-SMI 570.133.20             Driver Version: 570.133.20     CUDA Version: 12.8     |\n",
      "|-----------------------------------------+------------------------+----------------------+\n",
      "| GPU  Name                 Persistence-M | Bus-Id          Disp.A | Volatile Uncorr. ECC |\n",
      "| Fan  Temp   Perf          Pwr:Usage/Cap |           Memory-Usage | GPU-Util  Compute M. |\n",
      "|                                         |                        |               MIG M. |\n",
      "|=========================================+========================+======================|\n",
      "|   0  NVIDIA A10G                    On  |   00000000:00:1E.0 Off |                    0 |\n",
      "|  0%   33C    P0             60W /  300W |   16553MiB /  23028MiB |      0%      Default |\n",
      "|                                         |                        |                  N/A |\n",
      "+-----------------------------------------+------------------------+----------------------+\n",
      "                                                                                         \n",
      "+-----------------------------------------------------------------------------------------+\n",
      "| Processes:                                                                              |\n",
      "|  GPU   GI   CI              PID   Type   Process name                        GPU Memory |\n",
      "|        ID   ID                                                               Usage      |\n",
      "|=========================================================================================|\n",
      "|  No running processes found                                                             |\n",
      "+-----------------------------------------------------------------------------------------+\n"
     ]
    }
   ],
   "source": [
    "!nvidia-smi"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\"ready\":true}"
     ]
    }
   ],
   "source": [
    "!curl http://host.docker.internal:7670/v1/health/ready"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Interacting with the blueprint\n",
    "\n",
    "There are 2 ways to interact with `nv-ingest`, a python client and a CLI. Lets use the Python client first "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Installing the Python Client"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "%%bash\n",
    "pip install nv-ingest-client==25.6.2 \\\n",
    "    pymilvus[bulk_writer,model] \\\n",
    "    minio \\\n",
    "    tritonclient \\\n",
    "    langchain_milvus"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using the python client "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Each ingest job will include a set of stages. These stages define and configure the operations that will be performed during ingestion of the specified input files.\n",
    "\n",
    "- `extract` : Performs multimodal extractions from a document, including text, images, and tables.\n",
    "- `split` : Chunk the text into smaller chunks, useful for storing in a vector database for retrieval applications.\n",
    "- `dedup` : Identifies duplicate images in document that can be filtered to remove data redundancy.\n",
    "- `filter` : Filters out images that are likely not useful using some heuristics, including size and aspect ratio.\n",
    "- `embed` : Pass the text or table extractions through `\"nvidia/nv-embedqa-e5-v5` NIM to obtain its embeddings.\n",
    "- `store` : Save the extracted tables or images to MinIO, Milvus's storage system."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nv_ingest_client.client import Ingestor\n",
    "\n",
    "# Load a sample PDF to demonstrate NV-Ingest usage.\n",
    "ingestor = ( \n",
    "    Ingestor(message_client_hostname=\"host.docker.internal\", message_client_port=7670)\n",
    "    .files(\"./nv-ingest/data/multimodal_test.pdf\") # can be a list of files, or contain wildcards i.e. /some/path/*.pdf\n",
    "    .extract(\n",
    "        extract_text=True,\n",
    "        extract_tables=True,\n",
    "        extract_charts=True,\n",
    "        extract_images=True,\n",
    "    ).split(\n",
    "        tokenizer=\"meta-llama/Llama-3.2-1B\",\n",
    "        chunk_size=1024,\n",
    "        chunk_overlap=150,\n",
    "    ).embed( # whether to compute embeddings\n",
    "        text=True, tables=True\n",
    "    ) \n",
    ")\n",
    "\n",
    "# Result is a List[List[Dict]] - Each outer list Item [] is a file and each inner list Item [][] is an element in that file\n",
    "generated_metadata = ingestor.ingest()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'TestingDocument\\r\\nA sample document with headings and placeholder text\\r\\nIntroduction\\r\\nThis is a placeholder document that can be used for any purpose. It contains some \\r\\nheadings and some placeholder text to fill the space. The text is not important and contains \\r\\nno real value, but it is useful for testing. Below, we will have some simple tables and charts \\r\\nthat we can use to confirm Ingest is working as expected.\\r\\nTable 1\\r\\nThis table describes some animals, and some activities they might be doing in specific \\r\\nlocations.\\r\\nAnimal Activity Place\\r\\nGira@e Driving a car At the beach\\r\\nLion Putting on sunscreen At the park\\r\\nCat Jumping onto a laptop In a home o@ice\\r\\nDog Chasing a squirrel In the front yard\\r\\nChart 1\\r\\nThis chart shows some gadgets, and some very fictitious costs. Section One\\r\\nThis is the first section of the document. It has some more placeholder text to show how \\r\\nthe document looks like. The text is not meant to be meaningful or informative, but rather to \\r\\ndemonstrate the layout and formatting of the document.\\r\\n• This is the first bullet point\\r\\n• This is the second bullet point\\r\\n• This is the third bullet point\\r\\nSection Two\\r\\nThis is the second section of the document. It is more of the same as we’ve seen in the rest \\r\\nof the document. The content is meaningless, but the intent is to create a very simple \\r\\nsmoke test to ensure extraction is working as intended. This will be used in CI as time goes \\r\\non to ensure that changes we make to the library do not negatively impact our accuracy.\\r\\nTable 2\\r\\nThis table shows some popular colors that cars might come in.\\r\\nCar Color1 Color2 Color3\\r\\nCoupe White Silver Flat Gray\\r\\nSedan White Metallic Gray Matte Gray\\r\\nMinivan Gray Beige Black\\r\\nTruck Dark Gray Titanium Gray Charcoal\\r\\nConvertible Light Gray Graphite Slate Gray\\r\\nPicture\\r\\nBelow, is a high-quality picture of some shapes. Chart 2\\r\\nThis chart shows some average frequency ranges for speaker drivers.\\r\\nConclusion\\r\\nThis is the conclusion of the document. It has some more placeholder text, but the most \\r\\nimportant thing is that this is the conclusion. As we end this document, we should have \\r\\nbeen able to extract 2 tables, 2 charts, and some text including 3 bullet points.\\nThis chart shows some gadgets, and some very fictitious costs Gadgets and their cost   Hammer - Powerdrill - Bluetooth speaker - Minifridge - Premium desk fan Dollars $- - $20.00 - $40.00 - $60.00 - $80.00 - $100.00 - $120.00 - $140.00 - $160.00 Cost    Chart 1\\n| Table 1 |\\n| This table describes some animals, and some activities they might be doing in specific |\\n| locations. |\\n| Animal | Activity | Place |\\n| Giraffe | Driving a car | At the beach |\\n| Lion | Putting on sunscreen | At the park |\\n| Cat | Jumping onto a laptop | In a home office |\\n| Dog | Chasing a squirrel | In the front yard |\\n\\nimage_caption:[]\\nimage_caption:[]\\nBelow,is a high-quality picture of some shapes          Picture\\n| Table 2 |\\n| This table shows some popular colors that cars might come in |\\n| Car | Color1 | Color2 | Color3 |\\n| Coupe | White | Silver | Flat Gray |\\n| Sedan | White | Metallic Gray | Matte Gray |\\n| Minivan | Gray | Beige | Black |\\n| Truck | Dark Gray | Titanium Gray | Charcoal |\\n| Convertible | Light Gray | Graphite | Slate Gray |\\n\\nimage_caption:[]\\nimage_caption:[]\\nThis chart shows some average frequency ranges for speaker drivers. Frequency Ranges ofSpeaker Drivers   Tweeter - Midrange - Midwoofer - Subwoofer Hertz (log scale) 1 - 10 - 100 - 1000 - 10000 - 100000 FrequencyRange Start (Hz) - Frequency Range End (Hz) - Midwoofer    Chart2\\nimage_caption:[]\\n'"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from nv_ingest_client.util.process_json_files import ingest_json_results_to_blob\n",
    "\n",
    "# generated_metadata is the result of a batch of submitted files. We sample the first file metadata here for demonstration purposes.\n",
    "ingest_json_results_to_blob(generated_metadata[0])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explore the Outputs\n",
    "\n",
    "Let's explore elements of the NV-Ingest output. When data flows through an NV-Ingest pipeline, a number of extractions and transformations are performed. As the data is enriched, it is stored in rich metadata hierarchy. In the end, there will be a list of dictionaries, each of which represents a extracted type of information. The most common elements to extract from a dictionary in this hierarchy are the extracted content and the text representation of this content. The next few cells will demonstrate interacting with the metadata, pulling out these elements, and visualizing them. Note, when there is a -1 value present, this represents non-applicable positional resolution. Positive numbers represent valid positional data.\n",
    "\n",
    "For a more complete description of metadata elements, view the data dictionary.\n",
    "\n",
    "https://github.com/NVIDIA/nv-ingest/blob/main/docs/docs/extraction/content-metadata.md"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "def redact_metadata_helper(metadata: dict) -> dict:\n",
    "    \"\"\"A simple helper function to redact `metadata[\"content\"]` and metadata[\"embedding\"]' to improve readability.\"\"\"\n",
    "    \n",
    "    text_metadata_redact = metadata.copy()\n",
    "    text_metadata_redact[\"metadata\"][\"content\"] = \"<---Redacted for readability--->\"\n",
    "    text_metadata_redact[\"metadata\"][\"embedding\"] = \"<---Redacted for readability--->\"\n",
    "    \n",
    "    return text_metadata_redact"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explore Output - Text\n",
    "\n",
    "This cell depicts the full metadata hierarchy for a text extraction with redacted content to ease readability. Notice the following sections are populated with information:\n",
    "\n",
    "- `content` - The raw extracted content, text in this case - this section will always be populated with a successful job.\n",
    "- `content_metadata` - Describes the type of extraction and its position in the broader document - this section will always be populated with a successful job.\n",
    "- `source_metadata` - Describes the source document that is the basis of the ingest job.\n",
    "- `text_metadata` - Contain information about the text extraction, including detected language, among others - this section will only exist when `metadata['content_metadata']['document_type'] == 'text'`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'document_type': 'text',\n",
       " 'metadata': {'content': '<---Redacted for readability--->',\n",
       "  'content_url': '',\n",
       "  'embedding': '<---Redacted for readability--->',\n",
       "  'source_metadata': {'source_name': './nv-ingest/data/multimodal_test.pdf',\n",
       "   'source_id': './nv-ingest/data/multimodal_test.pdf',\n",
       "   'source_location': '',\n",
       "   'source_type': 'PDF',\n",
       "   'collection_id': '',\n",
       "   'date_created': '2025-06-17T18:13:04.715946',\n",
       "   'last_modified': '2025-06-17T18:13:04.715805',\n",
       "   'summary': '',\n",
       "   'partition_id': -1,\n",
       "   'access_level': -1},\n",
       "  'content_metadata': {'type': 'text',\n",
       "   'description': 'Unstructured text from PDF document.',\n",
       "   'page_number': -1,\n",
       "   'hierarchy': {'page_count': 3,\n",
       "    'page': -1,\n",
       "    'block': -1,\n",
       "    'line': -1,\n",
       "    'span': -1,\n",
       "    'nearby_objects': {'text': {'content': [], 'bbox': [], 'type': []},\n",
       "     'images': {'content': [], 'bbox': [], 'type': []},\n",
       "     'structured': {'content': [], 'bbox': [], 'type': []}}},\n",
       "   'subtype': ''},\n",
       "  'audio_metadata': None,\n",
       "  'text_metadata': {'text_type': 'document',\n",
       "   'summary': '',\n",
       "   'keywords': '',\n",
       "   'language': 'en',\n",
       "   'text_location': [-1, -1, -1, -1],\n",
       "   'text_location_max_dimensions': [-1, -1]},\n",
       "  'image_metadata': None,\n",
       "  'table_metadata': None,\n",
       "  'chart_metadata': None,\n",
       "  'error_metadata': None,\n",
       "  'info_message_metadata': None,\n",
       "  'debug_metadata': None,\n",
       "  'raise_on_failure': False}}"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "redacted_text_metadata = redact_metadata_helper(generated_metadata[0][0])  # First file, first element of elements found within file [0][0]. There are 9 total\n",
    "redacted_text_metadata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Explore Output - Tables\n",
    "\n",
    "This cell depicts the full metadata hierarchy for a table extraction with redacted content to ease readability. Notice the following sections are populated with information:\n",
    "\n",
    "- `content` - The raw extracted content, a base64 encoded image of the extracted table in this case - this section will always be populated with a successful job.\n",
    "- `content_metadata` - Describes the type of extraction and its position in the broader document - this section will always be populated with a successful job.\n",
    "- `source_metadata` - Describes the source and storage path of an extracted table in an S3 compliant object store.\n",
    "- `table_metadata` - Contains the text representation of the table, positional data, and other useful elements - this section will only exist when `metadata['content_metadata']['document_type'] == 'structured'`.\n",
    "\n",
    "Note, `table_metadata` will store chart and table extractions. The are distringuished by `metadata['content_metadata']['subtype']`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'document_type': 'structured',\n",
       " 'metadata': {'content': '<---Redacted for readability--->',\n",
       "  'content_url': '',\n",
       "  'embedding': '<---Redacted for readability--->',\n",
       "  'source_metadata': {'source_name': './nv-ingest/data/multimodal_test.pdf',\n",
       "   'source_id': './nv-ingest/data/multimodal_test.pdf',\n",
       "   'source_location': '',\n",
       "   'source_type': 'PDF',\n",
       "   'collection_id': '',\n",
       "   'date_created': '2025-06-17T18:13:04.715946',\n",
       "   'last_modified': '2025-06-17T18:13:04.715805',\n",
       "   'summary': '',\n",
       "   'partition_id': -1,\n",
       "   'access_level': -1},\n",
       "  'content_metadata': {'type': 'structured',\n",
       "   'description': 'Structured table extracted from PDF document.',\n",
       "   'page_number': 0,\n",
       "   'hierarchy': {'page_count': 3,\n",
       "    'page': 0,\n",
       "    'block': -1,\n",
       "    'line': -1,\n",
       "    'span': -1,\n",
       "    'nearby_objects': {'text': {'content': [], 'bbox': [], 'type': []},\n",
       "     'images': {'content': [], 'bbox': [], 'type': []},\n",
       "     'structured': {'content': [], 'bbox': [], 'type': []}}},\n",
       "   'subtype': 'table'},\n",
       "  'audio_metadata': None,\n",
       "  'text_metadata': None,\n",
       "  'image_metadata': None,\n",
       "  'table_metadata': {'caption': '',\n",
       "   'table_format': 'image',\n",
       "   'table_content': '| Table 1 |\\n| This table describes some animals, and some activities they might be doing in specific |\\n| locations. |\\n| Animal | Activity | Place |\\n| Giraffe | Driving a car | At the beach |\\n| Lion | Putting on sunscreen | At the park |\\n| Cat | Jumping onto a laptop | In a home office |\\n| Dog | Chasing a squirrel | In the front yard |\\n',\n",
       "   'table_content_format': 'pseudo_markdown',\n",
       "   'table_location': [92, 314, 697, 483],\n",
       "   'table_location_max_dimensions': [792, 1024],\n",
       "   'uploaded_image_uri': ''},\n",
       "  'chart_metadata': None,\n",
       "  'error_metadata': None,\n",
       "  'info_message_metadata': None,\n",
       "  'debug_metadata': None,\n",
       "  'raise_on_failure': False}}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "redacted_table_metadata = redact_metadata_helper(generated_metadata[0][2])  # First file, third element within file [0][2]. There are 9 total\n",
    "redacted_table_metadata"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using the CLI\n",
    "\n",
    "The CLI is another way to interact with nv-ingest. Notice that we have encoded tasks in the `--tasks` flag. This will store outputs in a `processed_docs` folder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
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      "None of PyTorch, TensorFlow >= 2.0, or Flax have been found. Models won't be available and only tokenizers, configuration and file/data utilities can be used.\n",
      "INFO:nv_ingest_client.nv_ingest_cli:Processing 1 documents.\n",
      "INFO:nv_ingest_client.nv_ingest_cli:Output will be written to: ./processed_docs\n",
      "Processing files: 100%|██████████| 1/1 [00:02<00:00,  2.03s/file, pages_per_sec=1.47]\n",
      "INFO:nv_ingest_client.cli.util.processing:message_broker_task_source: Avg: 0.81 ms, Median: 0.81 ms, Total Time: 0.81 ms, Total % of Trace Computation: 0.02%\n",
      "INFO:nv_ingest_client.cli.util.processing:broker_source_network_in: Avg: 8.17 ms, Median: 8.17 ms, Total Time: 8.17 ms, Total % of Trace Computation: 0.23%\n",
      "INFO:nv_ingest_client.cli.util.processing:metadata_injector: Avg: 3.29 ms, Median: 3.29 ms, Total Time: 3.29 ms, Total % of Trace Computation: 0.09%\n",
      "INFO:nv_ingest_client.cli.util.processing:metadata_injector_channel_in: Avg: 5.47 ms, Median: 5.47 ms, Total Time: 5.47 ms, Total % of Trace Computation: 0.15%\n",
      "INFO:nv_ingest_client.cli.util.processing:pdf_extraction: Avg: 333.40 ms, Median: 163.39 ms, Total Time: 1667.01 ms, Total % of Trace Computation: 46.24%\n",
      "INFO:nv_ingest_client.cli.util.processing:pdf_extraction_channel_in: Avg: 5.16 ms, Median: 5.16 ms, Total Time: 5.16 ms, Total % of Trace Computation: 0.14%\n",
      "INFO:nv_ingest_client.cli.util.processing:audio_extractor: Avg: 0.02 ms, Median: 0.02 ms, Total Time: 0.02 ms, Total % of Trace Computation: 0.00%\n",
      "INFO:nv_ingest_client.cli.util.processing:audio_extractor_channel_in: Avg: 5.89 ms, Median: 5.89 ms, Total Time: 5.89 ms, Total % of Trace Computation: 0.16%\n",
      "INFO:nv_ingest_client.cli.util.processing:docx_extractor: Avg: 0.02 ms, Median: 0.02 ms, Total Time: 0.02 ms, Total % of Trace Computation: 0.00%\n",
      "INFO:nv_ingest_client.cli.util.processing:docx_extractor_channel_in: Avg: 6.23 ms, Median: 6.23 ms, Total Time: 6.23 ms, Total % of Trace Computation: 0.17%\n",
      "INFO:nv_ingest_client.cli.util.processing:pptx_extractor: Avg: 0.02 ms, Median: 0.02 ms, Total Time: 0.02 ms, Total % of Trace Computation: 0.00%\n",
      "INFO:nv_ingest_client.cli.util.processing:pptx_extractor_channel_in: Avg: 6.06 ms, Median: 6.06 ms, Total Time: 6.06 ms, Total % of Trace Computation: 0.17%\n",
      "INFO:nv_ingest_client.cli.util.processing:image_extraction: Avg: 0.02 ms, Median: 0.02 ms, Total Time: 0.02 ms, Total % of Trace Computation: 0.00%\n",
      "INFO:nv_ingest_client.cli.util.processing:image_extraction_channel_in: Avg: 6.25 ms, Median: 6.25 ms, Total Time: 6.25 ms, Total % of Trace Computation: 0.17%\n",
      "INFO:nv_ingest_client.cli.util.processing:html_extractor: Avg: 0.02 ms, Median: 0.02 ms, Total Time: 0.02 ms, Total % of Trace Computation: 0.00%\n",
      "INFO:nv_ingest_client.cli.util.processing:html_extractor_channel_in: Avg: 6.35 ms, Median: 6.35 ms, Total Time: 6.35 ms, Total % of Trace Computation: 0.18%\n",
      "INFO:nv_ingest_client.cli.util.processing:infographic_extraction: Avg: 0.02 ms, Median: 0.02 ms, Total Time: 0.02 ms, Total % of Trace Computation: 0.00%\n",
      "INFO:nv_ingest_client.cli.util.processing:infographic_extraction_channel_in: Avg: 6.38 ms, Median: 6.38 ms, Total Time: 6.38 ms, Total % of Trace Computation: 0.18%\n",
      "INFO:nv_ingest_client.cli.util.processing:table_extraction: Avg: 269.08 ms, Median: 269.08 ms, Total Time: 538.17 ms, Total % of Trace Computation: 14.93%\n",
      "INFO:nv_ingest_client.cli.util.processing:table_extraction_channel_in: Avg: 6.32 ms, Median: 6.32 ms, Total Time: 6.32 ms, Total % of Trace Computation: 0.18%\n",
      "INFO:nv_ingest_client.cli.util.processing:chart_extraction: Avg: 425.12 ms, Median: 478.19 ms, Total Time: 1275.35 ms, Total % of Trace Computation: 35.37%\n",
      "INFO:nv_ingest_client.cli.util.processing:chart_extraction_channel_in: Avg: 7.17 ms, Median: 7.17 ms, Total Time: 7.17 ms, Total % of Trace Computation: 0.20%\n",
      "INFO:nv_ingest_client.cli.util.processing:image_filter: Avg: 0.02 ms, Median: 0.02 ms, Total Time: 0.02 ms, Total % of Trace Computation: 0.00%\n",
      "INFO:nv_ingest_client.cli.util.processing:image_filter_channel_in: Avg: 7.82 ms, Median: 7.82 ms, Total Time: 7.82 ms, Total % of Trace Computation: 0.22%\n",
      "INFO:nv_ingest_client.cli.util.processing:image_deduplication: Avg: 0.02 ms, Median: 0.02 ms, Total Time: 0.02 ms, Total % of Trace Computation: 0.00%\n",
      "INFO:nv_ingest_client.cli.util.processing:image_deduplication_channel_in: Avg: 7.35 ms, Median: 7.35 ms, Total Time: 7.35 ms, Total % of Trace Computation: 0.20%\n",
      "INFO:nv_ingest_client.cli.util.processing:text_splitter: Avg: 0.02 ms, Median: 0.02 ms, Total Time: 0.02 ms, Total % of Trace Computation: 0.00%\n",
      "INFO:nv_ingest_client.cli.util.processing:text_splitter_channel_in: Avg: 7.34 ms, Median: 7.34 ms, Total Time: 7.34 ms, Total % of Trace Computation: 0.20%\n",
      "INFO:nv_ingest_client.cli.util.processing:text_embedding: Avg: 0.02 ms, Median: 0.02 ms, Total Time: 0.02 ms, Total % of Trace Computation: 0.00%\n",
      "INFO:nv_ingest_client.cli.util.processing:text_embedding_channel_in: Avg: 7.27 ms, Median: 7.27 ms, Total Time: 7.27 ms, Total % of Trace Computation: 0.20%\n",
      "INFO:nv_ingest_client.cli.util.processing:image_captioning: Avg: 0.02 ms, Median: 0.02 ms, Total Time: 0.02 ms, Total % of Trace Computation: 0.00%\n",
      "INFO:nv_ingest_client.cli.util.processing:image_captioning_channel_in: Avg: 7.07 ms, Median: 7.07 ms, Total Time: 7.07 ms, Total % of Trace Computation: 0.20%\n",
      "INFO:nv_ingest_client.cli.util.processing:image_storage: Avg: 0.02 ms, Median: 0.02 ms, Total Time: 0.02 ms, Total % of Trace Computation: 0.00%\n",
      "INFO:nv_ingest_client.cli.util.processing:image_storage_channel_in: Avg: 7.41 ms, Median: 7.41 ms, Total Time: 7.41 ms, Total % of Trace Computation: 0.21%\n",
      "INFO:nv_ingest_client.cli.util.processing:embedding_storage: Avg: 0.02 ms, Median: 0.02 ms, Total Time: 0.02 ms, Total % of Trace Computation: 0.00%\n",
      "INFO:nv_ingest_client.cli.util.processing:embedding_storage_channel_in: Avg: 6.84 ms, Median: 6.84 ms, Total Time: 6.84 ms, Total % of Trace Computation: 0.19%\n",
      "INFO:nv_ingest_client.cli.util.processing:No unresolved time detected. Trace times account for the entire elapsed duration.\n",
      "INFO:nv_ingest_client.cli.util.processing:Processed 1 files in 2.04 seconds.\n",
      "INFO:nv_ingest_client.cli.util.processing:Total pages processed: 3\n",
      "INFO:nv_ingest_client.cli.util.processing:Throughput (Pages/sec): 1.47\n",
      "INFO:nv_ingest_client.cli.util.processing:Throughput (Files/sec): 0.49\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "\n",
    "nv-ingest-cli \\\n",
    "  --doc nv-ingest/data/multimodal_test.pdf \\\n",
    "  --output_directory ./processed_docs \\\n",
    "  --task='extract:{\"document_type\": \"pdf\", \"extract_method\": \"pdfium\", \"extract_tables\": \"true\", \"extract_images\": \"true\", \"extract_charts\": \"true\"}' \\\n",
    "  --client_host=host.docker.internal \\\n",
    "  --client_port=7670"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Building an E2E reranking and retreival pipeline\n",
    "\n",
    "Reranking is crucial for achieving high accuracy and efficiency in retrieval pipelines. It plays a vital role, particularly when the pipeline incorporates citations from diverse datastores, where each datastore may employ its own unique similarity scoring algorithm. Reranking serves two primary purposes:\n",
    "\n",
    "1. Improving accuracy for individual citations within each datastore.\n",
    "2. Integrating results from multiple datastores to provide a cohesive and relevant set of citations.\n",
    "\n",
    "We'll be using a couple different NIMs for this pipeline. These are a combination of models running locally and hosted on [build.nvidia.com]. However, all of these can be self hosted!\n",
    "1. `llama-3.2-nv-embedqa-1b-v2`: this will serve as our embedding model. `nv-ingest` will use this in the `VdbUploadTask`\n",
    "2. `nv-rerankqa-mistral-4b-v3`: this will be our reranking model\n",
    "3. `llama-3.1-nemotron-70b-instruct`: NVIDIA's new SOTA model for our LLM\n",
    "\n",
    "For this example, lets use the table in the `woods_frost.pdf`. For reference, here it is \n",
    "\n",
    "| # | Collection | Year |\n",
    "|---|------------|------|\n",
    "| 1 | A Boy's Will | 1913 |\n",
    "| 2 | North of Boston | 1914 |\n",
    "| 3 | Mountain Interval | 1916 |\n",
    "| 4 | New Hampshire | 1923 |\n",
    "| 5 | West Running Brook | 1928 |\n",
    "| 6 | A Further Range | 1937 |\n",
    "| 7 | A Witness Tree | 1942 |\n",
    "| 8 | In the Clearing | 1962 |\n",
    "| 9 | Steeple Bush | 1947 |\n",
    "| 10 | An Afterword | unknown |\n",
    "\n",
    "This table lists various poetry collections by Robert Frost, along with their publication years. Note that the year for \"An Afterword\" is listed as unknown.\n",
    "\n",
    "Note: this demo is hosted live on  [build.nvidia.com](https://build.nvidia.com/nvidia/multimodal-pdf-data-extraction-for-enterprise-rag)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Checking and installing dependancies"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3127aeb92c64   nvcr.io/nim/nvidia/llama-3.2-nv-embedqa-1b-v2:1.6.0          \"/opt/nim/start_serv…\"   20 hours ago   Up 11 minutes             0.0.0.0:8012->8000/tcp, [::]:8012->8000/tcp, 0.0.0.0:8013->8001/tcp, [::]:8013->8001/tcp, 0.0.0.0:8014->8002/tcp, [::]:8014->8002/tcp   nv-ingest-embedding-1\n"
     ]
    }
   ],
   "source": [
    "# ensure that we have the embedding model up\n",
    "!docker ps | grep embed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c535a610498c   milvusdb/milvus:v2.5.3-gpu                                   \"/tini -- milvus run…\"   20 hours ago   Up 30 minutes (healthy)   0.0.0.0:9091->9091/tcp, [::]:9091->9091/tcp, 0.0.0.0:19530->19530/tcp, [::]:19530->19530/tcp                                            milvus-standalone\n"
     ]
    }
   ],
   "source": [
    "# ensure we have milvusdb up\n",
    "!docker ps | grep milvusdb"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Testing with no pipeline\n",
    "\n",
    "Lets see what our LLM knows about Robert Frost's poetry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "# generate an API key from build.nvidia.com for hosted models\n",
    "import os\n",
    "\n",
    "# NOTE: You should surround this value with \"\"\n",
    "BUILD_API_KEY=<enter-key-here>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Here are the poetry collections of Robert Frost, listed in chronological order by publication year:\n",
      "\n",
      "1. **A Boy's Will** (1913)\n",
      "2. **North of Boston** (1914)\n",
      "3. **Mountain Interval** (1924)\n",
      "4. **New Hampshire** (1923) *won the Pulitzer Prize for Poetry in 1924*\n",
      "5. **Collected Poems** (1930)\n",
      "6. **Further Range** (1936) *won the Pulitzer Prize for Poetry in 1937*\n",
      "7. **A Witness Tree** (1942) *won the Pulitzer Prize for Poetry in 1943*\n",
      "8. **Come In, and Other Poems** (1943)\n",
      "9. **Steeple Bush** (1947)\n",
      "10. **Complete Poems of Robert Frost** (1949)\n",
      "11. **In the Clearing** (1962)\n",
      "12. **The Poetry of Robert Frost** (1969) *published posthumously, edited by Edward Connery Lathem*\n",
      "\n",
      "Note: There have been numerous posthumous collections, anthologies, and editions of Frost's poetry, but the above list includes the primary collections published during his lifetime."
     ]
    }
   ],
   "source": [
    "from openai import OpenAI\n",
    "\n",
    "client = OpenAI(\n",
    "  base_url = \"https://integrate.api.nvidia.com/v1\",\n",
    "  api_key = BUILD_API_KEY\n",
    ")\n",
    "\n",
    "completion = client.chat.completions.create(\n",
    "  model=\"nvidia/llama-3.1-nemotron-70b-instruct\",\n",
    "  messages=[{\"role\":\"user\",\"content\":\"What are Robert Frosts poetry collections. Provide the name and year.\"}],\n",
    "  temperature=0.5,\n",
    "  top_p=1,\n",
    "  max_tokens=1024,\n",
    "  stream=True\n",
    ")\n",
    "\n",
    "for chunk in completion:\n",
    "  if chunk.choices[0].delta.content is not None:\n",
    "    print(chunk.choices[0].delta.content, end=\"\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## With pipeline\n",
    "\n",
    "Our model got some of them but also included some selected works that we were not looking for. Lets see if we can improve this response"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pipeline section 1: Extraction\n",
    "\n",
    "We first extract the document metadata using nv-ingest."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nv_ingest_client.client import Ingestor\n",
    "from nv_ingest_client.util.file_processing.extract import extract_file_content\n",
    "\n",
    "file_content, file_type = extract_file_content(\"./nv-ingest/data/woods_frost.pdf\")\n",
    "\n",
    "# Load a sample PDF to demonstrate NV-Ingest usage.\n",
    "ingestor = (\n",
    "    Ingestor(message_client_hostname=\"host.docker.internal\", message_client_port=7670)\n",
    "    .files(\"./nv-ingest/data/woods_frost.pdf\") # can be a list of files, or contain wildcards i.e. /some/path/*.pdf\n",
    "    .extract(\n",
    "        extract_text=True,\n",
    "        extract_tables=True,\n",
    "        extract_charts=True,\n",
    "        extract_images=True,\n",
    "    ).split(\n",
    "        tokenizer=\"meta-llama/Llama-3.2-1B\",\n",
    "        chunk_size=1024,\n",
    "        chunk_overlap=150,\n",
    "    ).embed()\n",
    ")\n",
    "\n",
    "generated_metadata = ingestor.ingest()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pipeline section 2: Visualize metadata \n",
    "\n",
    "Below we provide some helper functions that can be used to analyze the metadata"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "from collections import Counter\n",
    "from typing import List, Dict, Any\n",
    "from base64 import b64decode\n",
    "from IPython.display import display, Image\n",
    "\n",
    "def count_metadata_types(metadata: List[Dict[str, Any]]) -> Dict[str, int]:\n",
    "    \"\"\"\n",
    "    Count the number of each metadata type in the generated metadata.\n",
    "    \"\"\"\n",
    "    return dict(Counter(item['document_type'] for item in metadata))\n",
    "\n",
    "def analyze_text_metadata(metadata: List[Dict[str, Any]]) -> Dict[str, Any]:\n",
    "    \"\"\"\n",
    "    Analyze text metadata and return key information.\n",
    "    \"\"\"\n",
    "    text_items = [item for item in metadata if item['document_type'] == 'text']\n",
    "    if not text_items:\n",
    "        return {\"error\": \"No text metadata found\"}\n",
    "    \n",
    "    text_item = text_items[0]['metadata']\n",
    "    return {\n",
    "        \"language\": text_item['text_metadata']['language'],\n",
    "        \"page_count\": text_item['content_metadata']['hierarchy']['page_count'],\n",
    "        \"source_name\": text_item['source_metadata']['source_name'],\n",
    "        \"content_preview\": text_item['content'][:200] + \"...\"  # First 200 characters\n",
    "    }\n",
    "\n",
    "def analyze_table_metadata(metadata: List[Dict[str, Any]]) -> List[Dict[str, Any]]:\n",
    "    \"\"\"\n",
    "    Analyze table metadata and return key information for each table.\n",
    "    \"\"\"\n",
    "    table_items = [item['metadata'] for item in metadata \n",
    "                   if item['document_type'] == 'structured' \n",
    "                   and item['metadata']['content_metadata']['subtype'] == 'table']\n",
    "    \n",
    "    return [{\n",
    "        \"content_preview\": table['table_metadata']['table_content'][:100] + \"...\",\n",
    "        \"location\": table['table_metadata']['table_location'],\n",
    "        \"page_number\": table['content_metadata']['page_number']\n",
    "    } for table in table_items]\n",
    "\n",
    "def analyze_chart_metadata(metadata: List[Dict[str, Any]]) -> List[Dict[str, Any]]:\n",
    "    \"\"\"\n",
    "    Analyze chart metadata and return key information for each chart.\n",
    "    \"\"\"\n",
    "    chart_items = [item['metadata'] for item in metadata \n",
    "                   if item['document_type'] == 'structured' \n",
    "                   and item['metadata']['content_metadata']['subtype'] == 'chart']\n",
    "    \n",
    "    return [{\n",
    "        \"content_preview\": chart['table_metadata']['table_content'][:100] + \"...\",\n",
    "        \"location\": chart['table_metadata']['table_location'],\n",
    "        \"page_number\": chart['content_metadata']['page_number']\n",
    "    } for chart in chart_items]\n",
    "\n",
    "def analyze_image_metadata(metadata: List[Dict[str, Any]]) -> List[Dict[str, Any]]:\n",
    "    \"\"\"\n",
    "    Analyze image metadata and return key information for each image.\n",
    "    \"\"\"\n",
    "    image_items = [item['metadata'] for item in metadata if item['document_type'] == 'image']\n",
    "    \n",
    "    return [{\n",
    "        \"image_type\": img['image_metadata']['image_type'],\n",
    "        \"dimensions\": f\"{img['image_metadata']['width']}x{img['image_metadata']['height']}\",\n",
    "        \"location\": img['image_metadata']['image_location'],\n",
    "        \"page_number\": img['content_metadata']['page_number']\n",
    "    } for img in image_items]\n",
    "\n",
    "def display_image(metadata: List[Dict[str, Any]], index: int = 0):\n",
    "    \"\"\"\n",
    "    Display an image from the metadata at the specified index.\n",
    "    \"\"\"\n",
    "    image_items = [item for item in metadata if item['document_type'] == 'image']\n",
    "    if index < 0 or index >= len(image_items):\n",
    "        print(f\"Invalid index. There are {len(image_items)} images.\")\n",
    "        return\n",
    "    \n",
    "    image_data = b64decode(image_items[index]['metadata']['content'])\n",
    "    display(Image(image_data))\n",
    "\n",
    "def comprehensive_metadata_analysis(metadata: List[Dict[str, Any]]) -> Dict[str, Any]:\n",
    "    \"\"\"\n",
    "    Perform a comprehensive analysis of the metadata and return a summary.\n",
    "    \"\"\"\n",
    "    return {\n",
    "        \"type_counts\": count_metadata_types(metadata),\n",
    "        \"text_analysis\": analyze_text_metadata(metadata),\n",
    "        \"table_analysis\": analyze_table_metadata(metadata),\n",
    "        \"chart_analysis\": analyze_chart_metadata(metadata),\n",
    "        \"image_analysis\": analyze_image_metadata(metadata)\n",
    "    }\n",
    "\n",
    "def print_metadata_summary(analysis: Dict[str, Any]):\n",
    "    \"\"\"\n",
    "    Print a formatted summary of the metadata analysis.\n",
    "    \"\"\"\n",
    "    print(\"NV-Ingest Metadata Analysis Summary\")\n",
    "    print(\"===================================\")\n",
    "    \n",
    "    print(\"\\nMetadata Type Counts:\")\n",
    "    for doc_type, count in analysis['type_counts'].items():\n",
    "        print(f\"  {doc_type}: {count}\")\n",
    "    \n",
    "    print(\"\\nText Analysis:\")\n",
    "    text_analysis = analysis['text_analysis']\n",
    "    print(f\"  Language: {text_analysis['language']}\")\n",
    "    print(f\"  Page Count: {text_analysis['page_count']}\")\n",
    "    print(f\"  Source Name: {text_analysis['source_name']}\")\n",
    "    print(f\"  Content Preview: {text_analysis['content_preview']}\")\n",
    "    \n",
    "    print(\"\\nTable Analysis:\")\n",
    "    for i, table in enumerate(analysis['table_analysis'], 1):\n",
    "        print(f\"  Table {i}:\")\n",
    "        print(f\"    Content Preview: {table['content_preview']}\")\n",
    "        print(f\"    Location: {table['location']}\")\n",
    "        print(f\"    Page Number: {table['page_number']}\")\n",
    "    \n",
    "    print(\"\\nChart Analysis:\")\n",
    "    for i, chart in enumerate(analysis['chart_analysis'], 1):\n",
    "        print(f\"  Chart {i}:\")\n",
    "        print(f\"    Content Preview: {chart['content_preview']}\")\n",
    "        print(f\"    Location: {chart['location']}\")\n",
    "        print(f\"    Page Number: {chart['page_number']}\")\n",
    "    \n",
    "    print(\"\\nImage Analysis:\")\n",
    "    for i, image in enumerate(analysis['image_analysis'], 1):\n",
    "        print(f\"  Image {i}:\")\n",
    "        print(f\"    Type: {image['image_type']}\")\n",
    "        print(f\"    Dimensions: {image['dimensions']}\")\n",
    "        print(f\"    Location: {image['location']}\")\n",
    "        print(f\"    Page Number: {image['page_number']}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NV-Ingest Metadata Analysis Summary\n",
      "===================================\n",
      "\n",
      "Metadata Type Counts:\n",
      "  text: 1\n",
      "  structured: 1\n",
      "  image: 4\n",
      "\n",
      "Text Analysis:\n",
      "  Language: en\n",
      "  Page Count: 2\n",
      "  Source Name: ./nv-ingest/data/woods_frost.pdf\n",
      "  Content Preview: Stopping by Woods on a Snowy Evening, By Robert Frost\n",
      "Figure 1: Snowy Woods\n",
      "Whose woods these are I think I know. His house is in the village though; He will not see me \n",
      "stopping here; To watch his...\n",
      "\n",
      "Table Analysis:\n",
      "  Table 1:\n",
      "    Content Preview: | # | Collection | Year |\n",
      "| 1 | A Boy's Will | 1913 |\n",
      "| 2 | North of Boston | 1914 |\n",
      "| 3 | Mountain ...\n",
      "    Location: [89, 29, 697, 379]\n",
      "    Page Number: 1\n",
      "\n",
      "Chart Analysis:\n",
      "\n",
      "Image Analysis:\n",
      "  Image 1:\n",
      "    Type: png\n",
      "    Dimensions: 227x166\n",
      "    Location: [72, 91, 299, 257]\n",
      "    Page Number: 0\n",
      "  Image 2:\n",
      "    Type: png\n",
      "    Dimensions: 129x194\n",
      "    Location: [72, 476, 201, 670]\n",
      "    Page Number: 0\n",
      "  Image 3:\n",
      "    Type: png\n",
      "    Dimensions: 483x169\n",
      "    Location: [58, 288, 541, 457]\n",
      "    Page Number: 0\n",
      "  Image 4:\n",
      "    Type: png\n",
      "    Dimensions: 483x236\n",
      "    Location: [58, 72, 541, 308]\n",
      "    Page Number: 1\n"
     ]
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
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     "metadata": {},
     "output_type": "display_data"
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   ],
   "source": [
    "analysis = comprehensive_metadata_analysis(generated_metadata[0])  # One analyze first file result\n",
    "print_metadata_summary(analysis)\n",
    "\n",
    "# To display an image:\n",
    "display_image(generated_metadata[0], 0)  # Display the first image"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pipeline section 3: Embedding, Storage, and Saving to a Vector DB\n",
    "\n",
    "We now create our Milvus collection and bulk upload our nv-ingest `generated_metadata` to the collection from Minio."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [],
   "source": [
    "from nv_ingest_client.util.milvus import create_nvingest_collection, write_to_nvingest_collection\n",
    "\n",
    "sparse = False\n",
    "COLLECTION_NAME=\"nv_ingest_collection\"\n",
    "\n",
    "# Create the Milvus collection\n",
    "schema = create_nvingest_collection(COLLECTION_NAME, f\"http://host.docker.internal:19530\", sparse=sparse, dense_dim=2048)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Index the nv-ingest generated_metadata to the Milvus collection\n",
    "write_to_nvingest_collection(generated_metadata, COLLECTION_NAME, sparse=sparse, milvus_uri=f\"http://host.docker.internal:19530\", minio_endpoint=\"host.docker.internal:9000\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Pipeline section 4: Connect to MilvusDB and the embedding model to start querying"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/ubuntu/.pyenv/versions/3.12.11/lib/python3.12/site-packages/langchain_nvidia_ai_endpoints/_common.py:242: UserWarning: Default model is set as: nvidia/llama-3.2-nv-embedqa-1b-v2. \n",
      "Set model using model parameter. \n",
      "To get available models use available_models property.\n",
      "  warnings.warn(\n",
      "2025-06-17 18:15:15,085 [DEBUG][_create_connection]: Created new connection using: 9add41a258b44dbba039250be903ac41 (async_milvus_client.py:599)\n"
     ]
    }
   ],
   "source": [
    "import os\n",
    "from langchain_nvidia_ai_endpoints import NVIDIAEmbeddings, NVIDIARerank, ChatNVIDIA\n",
    "from langchain_milvus import Milvus\n",
    "\n",
    "# TODO: Add your NVIDIA API key here\n",
    "os.environ[\"NVIDIA_API_KEY\"] = BUILD_API_KEY\n",
    "\n",
    "embedding = NVIDIAEmbeddings(base_url=\"http://host.docker.internal:8012/v1\")\n",
    "\n",
    "reranker = NVIDIARerank(model=\"nvidia/nv-rerankqa-mistral-4b-v3\")\n",
    "\n",
    "llm = ChatNVIDIA(model=\"nvidia/llama-3.1-nemotron-70b-instruct\")\n",
    "\n",
    "vectorstore = Milvus(\n",
    "    embedding_function=embedding,\n",
    "    collection_name=COLLECTION_NAME,\n",
    "    primary_field = \"pk\",\n",
    "    vector_field = \"vector\",\n",
    "    text_field=\"text\",\n",
    "    connection_args={\"uri\": \"http://host.docker.internal:19530\"},\n",
    ")\n",
    "retriever = vectorstore.as_retriever()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Based on the retrieved context, here are Robert Frost's poetry collections with their corresponding years:\n",
      "\n",
      "1. **A Boy's Will** (1913)\n",
      "2. **North of Boston** (1914)\n",
      "3. **Mountain Interval** (1916)\n",
      "4. **New Hampshire** (1923)\n",
      "5. **West Running Brook** (1928)\n",
      "6. **A Further Range** (1937)\n",
      "7. **A Witness Tree** (1942)\n",
      "8. **Steeple Bush** (1947)\n",
      "9. **In the Clearing** (1962)\n",
      "10. **An Afterword** (unknown/year not specified)\n"
     ]
    }
   ],
   "source": [
    "from langchain_core.prompts import PromptTemplate\n",
    "from langchain_core.runnables import RunnablePassthrough\n",
    "from langchain_core.output_parsers import StrOutputParser\n",
    "\n",
    "template = (\n",
    "    \"You are an assistant for question-answering tasks. \"\n",
    "    \"Use the following pieces of retrieved context to answer \"\n",
    "    \"the question. If you don't know the answer, say that you \"\n",
    "    \"don't know. Keep the answer concise.\"\n",
    "    \"\\n\\n\"\n",
    "    \"{context}\"\n",
    "    \"Question: {question}\"\n",
    ")\n",
    "\n",
    "prompt = PromptTemplate.from_template(template)\n",
    "\n",
    "rag_chain = (\n",
    "    {\"context\": retriever, \"question\": RunnablePassthrough()}\n",
    "    | prompt\n",
    "    | llm\n",
    "    | StrOutputParser()\n",
    ")\n",
    "resp=rag_chain.invoke(\"What are Robert Frosts poetry collections. Provide the name and year.?\")\n",
    "\n",
    "print(resp)"
   ]
  }
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